An IoT based Intelligent Traffic Congestion Control System for Road Crossings

Abstract

Traffic congestion is one of the major issues with the public transportation system in recent time. The traffic congestion has a negative impact on the productivity, competitiveness and economic growth of a country. Hence traffic congestion control has become an important area of research and significant number of solutions to this problem came out of various research efforts in the said field over the past few decades. Among these, vehicle-to-vehicle (V2V) communication based approaches cannot accurately estimate the density of traffic congestion. On the other hand, the traffic signaling systems having predetermined fixed operation time cannot manage the traffic volume changing over time and thus, long traffic queues are generated at the road crossings. To address the above mentioned issue, this paper proposes an internet-of-things (IoT) based intelligent traffic congestion control system that dynamically sets the signal operation time based on the measured values of traffic congestion density. Moreover, a novel technique of measuring the density of traffic congestion created at the road crossings is also presented in this paper.

Full text

An IoT based Intelligent Traffic Congestion Control
System for Road Crossings
Pampa Sadhukhan
School of Mobile Computing & Communication
Jadavpur University, India - 700032.
email: pampa.sadhukhan@ieee.org
Firoj Gazi
School of Mobile Computing & Communication
Jadavpur University, India - 700032.
email: firojgazi123@gmail.com
Abstract—Traffic congestion is one of the major issues with the
public transportation system in recent time. The traffic conges-
tion has a negative impact on the productivity, competitiveness
and economic growth of a country. Hence traffic congestion
control has become an important area of research and significant
number of solutions to this problem came out of various research
efforts in the said field over the past few decades. Among these,
vehicle-to-vehicle (V2V) communication based approaches cannot
accurately estimate the density of traffic congestion. On the other
hand, the traffic signaling systems having predetermined fixed op-
eration time cannot manage the traffic volume changing over time
and thus, long traffic queues are generated at the road crossings.
To address the above mentioned issue, this paper proposes an
internet-of-things (IoT) based intelligent traffic congestion control
system that dynamically sets the signal operation time based on
the measured values of traffic congestion density. Moreover, a
novel technique of measuring the density of traffic congestion
created at the road crossings is also presented in this paper.
Index Terms—traffic congestion, traffic management, vehicle
queue, ultrasonic sensor node (USN), Wi-Fi.
I. INTRODUCTION
Nowadays, the transportation systems are an essential
part of human activities. But, transportation infrastructure
in the urban areas is almost saturated due to the lack of
land resources and growing number of vehicles on the road.
Because of this saturation, various traffic-related problems
have been erupted in the urban areas where people need to
move very fast from one place to another. One of the major
issues with the public transportation system in recent time is
traffic congestion. The traffic congestion not only increases
the fuel consumption but also the risk of heart attacks [1].
Moreover, the traffic congestion delay very badly affects
human activities and thus, slows down the productivity,
competitiveness and overall growth of a country. A very well
known technique to address the traffic congestion problem
is adding new infrastructure by constructing new roads as
well as improving the existing infrastructure by widening the
roads. However, the limited availability of the land resources
in the urban areas, have made construction of new highway
or road within the cities is almost impossible. Moreover, the
construction of over bridge or tunnel is very expensive and it
has been found that the improvement in infrastructure always
lags behind the increase in traffic.
Thus, a lot of researchers have paid attention to the traffic
congestion issue of public transportation system over the
past few decades. Various solutions to road traffic conges-
tion detection and traffic management have been proposed
in literature by using the information and communication
technologies (ICT) along with internet-of-things (IoT) devices
to improve the effectiveness of the transportation system.
Among the existing techniques to address the traffic conges-
tion problem, vehicle-to-vehicle (V2V) communication based
approaches cannot accurately estimate the traffic congestion
condition (i.e., high, medium etc.) as it relies on significant
number of estimated traffic density information exchanges
among the vehicles and also correlating the collected traffic
density estimates [2, 3, 4, 5, 6, 7]. The road crossings in
most of the countries still use traffic signaling systems loaded
with statically determined fixed operation time, which cannot
manage the traffic volume varying over time and thus long
traffic queues are generated at the road crossings [8]. We,
therefore, propose in this paper an IoT based intelligent traffic
congestion control system to address the above mentioned
issue. Our proposed traffic congestion control system, at first,
attempts to measure the density of traffic congestion created
at road crossings by using ultrasonic sensor node (USN) and
then dynamically sets the signal operation time based on the
measured values of traffic congestion density.
II. RELATED WORK
Numerous IoT based traffic congestion monitoring and
management systems have been proposed in literature over
the past few years. Among these systems, a wireless sensor
network based framework for collaborative collection, fusion
and storage of city traffic information has been developed by
the researchers in [9]. The authors in [9] have shown that the
proposed city intelligent transportation system is more flexible
and reliable compared to the other existing city transportation
system. The researchers have provided future research direc-
tion on the emergency response scheme and transport priority
scheme. The researchers in [10] have proposed a framework
for the road vehicle traffic monitoring via smart phone based
measurement system and the usage-based insurance (UBI).
The purpose of this proposed framework is to model, predict,
and control the traffic flow. This framework consists of seven
layers, spanning from the physical smart phones and servers
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to the overall business model at the top layer. The design of a
modern traffic monitoring system that can optimize the road
traffic flow in order to meet the current and future necessities
for road travel is proposed in [11]. The researchers in [11] have
shown that their proposed system increases the efficiency of
the monitoring process of the road traffic conditions by pro-
viding permanent knowledge of the meteorological parameters
of different zones. In [12], the researchers have developed
a vehicle detection and classification system for low-speed
congested traffic. Their proposed system uses the low-cost
triaxial anisotropic magneto resistive sensor. A novel fixed
threshold state machine algorithm is applied in this proposed
system to detect vehicles within a single lane and also to
segment the vehicle signals effectively based on the time of
vehiclesentry and exit into the sensor monitoring area.
The researchers in [13] have proposed an intelligent traffic
congestion monitoring measurement system called Traffic-
Monitor to measure the real time road traffic congestion by
using RFID reader, wireless router, wireless coordinator and
GSM technology. The proposed system uses a probe vehicle at
which a RFID tag is attached. The probe vehicle measures the
level of traffic congestion from its speed over a stretch of road
and its average waiting time at the road crossing. To calculate
the speed of the probe vehicle and its average waiting time
at road crossing, a wireless router and a wireless coordinator
are deployed across the stretch of road which is 200 meters
long. The central monitoring system receives real time traffic
congestion scenario from the wireless coordinator via GSM
modules attached to both central monitoring system as well as
wireless coordinator. The use of probe vehicle for each part of
road between two junctions makes the proposed system very
costly. Moreover, the proposed system does not provide any
solution to curb the traffic congestion.
A context-aware approach to monitor real-time road traffic
density and dynamically manage the traffic signals to improve
the traffic efficiency has been proposed in [14]. The proposed
system uses sensor array to sense the traffic density and various
environmental conditions and then transmit those information
to an android phone over the Bluetooth connectivity. The
android phone is responsible for collecting traffic density infor-
mation for a certain locality and then sending it to the server
over the Internet. The central server processes and analyzes
those huge traffic data by applying data mining techniques
to detect traffic congestion and manages the traffic signals
according to the levels of detected traffic congestion. The
transmission of the traffic density information from android
phone to central server over the Internet may take longer than
usual time, which in turn reduces the efficiency of proposed
congestion control mechanism. A prototype of a model to
reduce the traffic congestion has been presented in [15].
The proposed model attempts to solve the problem of traffic
congestion at four way road crossings by sensing the traffic
load on the road using IR sensor and taking the best fitted
decision based on the traffic load on the road. However, the
detection of traffic load on the road by using IR sensor only is
not a reliable solution. Moreover the proposed system works
Fig. 1. Architecture of proposed traffic congestion control system
locally, i.e., it cannot provide city wise congestion control
report.
The prototype of an intelligent traffic congestion control
system that uses RFID, Zigbee and GSM module has been
proposed in [16]. In this proposed system, each vehicle must
be equipped with RFID tag in order to measure the volume
of traffic congestion and also to detect the stolen vehicle via
the RFID readers deployed at various parts of the road. The
proposed system dynamically adjusts green signal duration for
a particular road based on measured volume of traffic conges-
tion to reduce the traffic congestion. It can detect some stolen
vehicle if that vehicle comes within the range of some RFID
reader deployed at the road and upon detecting such vehicle it
would send SMS to the police station via the attached GSM
module. On the other hand, Zigbee transmitter is attached at
the emergency vehicle and Zigbee receiver is deployed at road
junction to make clearance path of the emergency vehicle
whenever it approaches the road junction. The major issue
with this proposed system is that the RFID readers deployed
at two sides of some wide road cannot properly measure the
traffic volume since the vehicles standing at the middle queues
should not come within the range of RFID reader.Because of
the above mentioned reason, the detection of stolen vehicle
may also fail.
III. PROPOSED TRAFFI C CONGESTION CONTROL SYSTEM
The architecture of the proposed system is shown in fig.
1 and it consists of two modules. These are traffic density
monitoring module (TDMM) and traffic management module
International Conference on Communication, Computing and Internet of Things (IC3IoT) 405

Fig. 2. Block diagram of traffic density monitoring module (TDMM)
(TMM). The role of TDMM is to measure the length of
traffic queue created in front of signal cross-over point in
order to determine the density of traffic congestion (i.e., low,
medium, high etc.). On the other hand, TMM attempts to
dynamically adjust the operation time of traffic signals based
on the estimated density of traffic congestion on different
roads connecting to the crossings section in order to curb
the congestion properly. The detailed design and functional
architecture of these two modules are presented below.
A. Traffic density monitoring module (TDMM)
This module uses an ultrasonic sensor to measure the
length of vehicle queue. TDMM contains a microcontroller
for processing the data collected from the sensor node and
also a Wi-Fi module for sending data to TMM either directly
if it is within the communication range of TMM or via some
other TDMMs. Fig. 1 also shows that a set of TDMMs are
deployed at certain predetermined distances (such as 50 meter,
100 meter, 150 meter and so on) away of the signal crossing
on only one side of the incoming roads (if it is one-way) or
on the side of incoming direction (if it is two-way road). The
number of TDMMs to be placed is determined according to the
general statistics of the traffic volume at each road crossing.
The ultrasonic sensor node (USN) periodically emits the
sound waves in the range of 25 −50 KHz. It is used to
detect the presence or absence of nearby standing vehicle by
measuring the distance from the difference of transmit time
and reception time of its emitted signal. Thus, the presence of
vehicle queue is detected by TDMM if the following condition
is satisfied.
Measured distance (dm)<reference distance (dr),
where reference distance is equals to the width of road and
measured distance is estimated as follows.
dm=C×(tr−ts),
where C is the speed of sound waves and, trand tsindicates
the reception time and transmit time of the emitted signal of
USN respectively. TDMMs are placed at certain height above
the ground so that the USN can receive the direct reflection
Fig. 3. Work flow diagram of detecting the presence of vehicle queue and
communicating it to TMM.
of its emitted signal in case the vehicle queue has reached
or exceeded its position. Each TDMM is assigned a unique
id whose structure is <local TDMM id >@<road id >.
The local TDMM id is a numeric value taken from the set
of numbers {1,2,···,P}in such a way that the TDMM at
shorter distance would be assigned lower value and Pis the
number of TDMMs deployed on a certain road. On the other
hand, road id is the name of road in string format. If the
presence of vehicle queue is detected by some TDMM, it
would construct a packet containing its unique id and the
current timestamp and then broadcasts that packet through the
Wi-Fi module. Apart from broadcasting its own packets, each
TDMM would broadcast the packets received from the other
406 International Conference on Communication, Computing and Internet of Things (IC3IoT)

Fig. 4. Block diagram of traffic management module (TMM)
TDMMs if following two conditions are satisfied.
1) TP>T
C−tth, where TP,TCand tth indicates the
timestamp value contained within the packet, the value
of current timestamp and the value of threshold time
respectively. The value of threshold time (tth) is set in
such a way that the density of traffic congestion should
be measured by considering the recent packets only.
2) The TDMM is located on at longer distances on same
road which is verified by the following two constraints.
a) road id of remote TDMM ≡road id of local
TDMM
b) local TDMM id of remote TDMM >TDMM id of
local TDMM.
The work flow diagram of detecting the presence of vehicle
queue and communicating it to TMM is depicted in Fig. 3
B. Traffic Management Module (TMM)
This software module is deployed on a Wi-Fi enabled laptop
or work station which is positioned at the road crossing as
shown in fig. 1. TMM is connected through its serial port to a
microcontroller, which is attached to the signal LED via some
relay module as depicted by fig. 4. The relay module is an
electromagnetic switch which is operated by some safe low-
voltage circuit in order to control a high-voltage circuit. TMM
is responsible for estimating the density of traffic congestion
which is labeled as low, medium, high and so on, on the
various roads and then dynamically sets the operation time
of the traffic signals based on the values of estimated density
of the traffic congestion on different roads. A suitable mapping
between various values of the estimated density of traffic
congestion and their corresponding green signal periods is
illustrated in table I. TMM adopts the following algorithm
to perform the above mentioned tasks.
Algorithm 1: Estimation of traffic congestion density and its
management:
1) Extract the timestamp value of each packet (TP) and
compare it with current timestamp (TC).
a) Ignore packet if TP≤TC−tth ,where tth is
predefined threshold time.
b) Otherwise execute the following steps.
TABLE I
CORRESPONDENCE BETWEEN TRAFFIC CONGESTION DENSITY AND
GREEN SIGNAL PERIOD
Traffic Congestion Density Green Signal Period
Low 20 Seconds
Medium 30 Seconds
High 50 Seconds
Very high 80 Seconds
i) Examine the unique id of source TDMM. Clas-
sify all received packets according to the road
id.
ii) Traffic congestion density on each road having
exactly three TDMMs, is estimated based on
following analysis.
•No TDMM indicates presence of vehicle
queue, i.e., traffic queue is shorter than the
position of first TDMM. Traffic congestion
density is set to low.
•Only first TDMM indicates presence of ve-
hicle queue, i.e., length of traffic queue is
between the position of first TDMM and
second TDMM. Traffic congestion density is
set to medium.
•First and second TDMMs only indicate pres-
ence of vehicle queue, i.e., length of traffic
queue is between the position of second
TDMM and third TDMM. Traffic congestion
density is set to high.
•All the TDMMs indicate presence of vehicle
queue, i.e., traffic queue exceeds the position
of last TDMM. Traffic congestion density is
set to very high.
2) TMM determines the operation time of the traffic signals
based on the values of the estimated density of traffic
congestion and loads these values into the microcon-
troller.
IV. CONCLUSION AND FUTURE WORK
Traffic congestion is one of the major issues with the
public transportation system of all developing countries in
recent time, as it not only increases the fuel consumption
but also the air pollution as well as the risk of heart attack.
This paper presents an IoT-based intelligent traffic congestion
control system in order to reduce the congestion delay via
dynamic management of traffic signals at the road crossings.
The proposed congestion control system dynamically sets
signal operation time based on the estimated values of traffic
congestion density and it also employs a novel technique of
estimating the density of traffic congestion by using USN.
However, only the design part of the proposed system is
presented in this paper. No experimental results have been
provided to demonstrate the effectiveness of the proposed
system. The evaluation of performances of this proposed
congestion control system through some test bed is our future
goal of research.
International Conference on Communication, Computing and Internet of Things (IC3IoT) 407

ACKNOWLEDGMENTS
The authors gratefully acknowledge the facilities and sup-
port provided by the Director and all other staff members
of the School of Mobile Computing and Communication,
Jadavpur University, a Centre of Excellence set up under
the University with potential for Excellence Scheme of the
UGC.
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